Machine Learning With Computer Networks: Techniques, Datasets, and Models

Machine learning has found many applications in network contexts. These include solving optimisation problems and managing network operations. Conversely, networks are essential for facilitating machine learning training and inference, whether performed centrally or in a distributed fashion. To cond...

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Bibliographic Details
Published in:IEEE access 2024, Vol.12, p.54673-54720
Main Authors: Afifi, Haitham, Pochaba, Sabrina, Boltres, Andreas, Laniewski, Dominic, Haberer, Janek, Paeleke, Leonard, Poorzare, Reza, Stolpmann, Daniel, Wehner, Nikolas, Redder, Adrian, Samikwa, Eric, Seufert, Michael
Format: Article
Language:English
Subjects:
Artificial intelligence
Computational modeling
Computer networking
Computer networks
Data models
Datasets
Machine learning
metrics
Supervised learning
Task analysis
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Summary:Machine learning has found many applications in network contexts. These include solving optimisation problems and managing network operations. Conversely, networks are essential for facilitating machine learning training and inference, whether performed centrally or in a distributed fashion. To conduct rigorous research in this area, researchers must have a comprehensive understanding of fundamental techniques, specific frameworks, and access to relevant datasets. Additionally, access to training data can serve as a benchmark or a springboard for further investigation. All these techniques are summarized in this article; serving as a primer paper and hopefully providing an efficient start for anybody doing research regarding machine learning for networks or using networks for machine learning.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3384460